Tackling Policy Leakage and Targeting Hot Spots Could Be Key to Addressing the “Wicked” Challenge of Nutrient Pollution from Corn Production in the United States
Bibliographic record
Abstract
Abstract Reducing nutrient loss from agriculture to improve water quality requires a combination of management practices. However, it has been unclear what pattern of mitigation is likely to emerge from different policies, individually and combined, and what are the impacts on land use and farm returns at both local and national levels. We address this research gap by developing an integrated multiscale framework that evaluates alternative nitrogen loss management policies for corn production in the United States. This approach combines site- and practice-specific agroecosystem processes with a grid-resolving economic model to identify locations that can be prioritized to increase the economic efficiency of the policies. We find that regional measures, while effective at reducing nitrogen loss locally, may inadvertently displace corn production to areas where nitrogen fertilizer productivity is lower and nutrient loss rates are higher, thereby offsetting the overall effectiveness of the nutrient management strategy. This spatial spillover effect can, however, be mitigated by combining regional measures with broader nationwide policies. For instance, the combination of wetland restoration, split fertilizer application and a nitrogen loss tax could reduce nitrate nitrogen loading in the Mississippi River by 30%, with only a modest increase in corn prices (less than 2%).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".